In their writings, Finn Kydland and Edward C. Prescott (K-P), the 2004 Nobel laureates in economics, had hypothesized that a major cause behind boom-bust cycles is technology shocks. In order to assess the importance of this claim, which they labeled the theory of real business cycles, K-P employed the Solow growth model (after Robert Solow, the 1987 Nobel laureate), which in turn is based on the Cobb-Douglas production function of the following type:
Y = A*K(1–a)*Na,
where Y is real output, A is a technology factor, K is the capital stock, and N is the number of workers employed. The a is a parameter. Mainstream economists hypothesize that in the real world there are relationships between various economic variables. These relations could be depicted via constants labeled parameters.
For instance, the relation between personal consumption expenditure and income after tax can be hypothesized as:
Personal consumption = α*income after tax, with α being a parameter.
Thus, if α is 0.8, this would imply that for an income after tax of $100, personal consumption is $80.
The parameter α, is ascertained with the help of a statistical method called regression analysis. The statistical method also provides the verification whether the obtained number is a valid estimate of the true parameter in the real world.
Instead of employing conventional statistical methods for the estimation of the parameter α, K-P introduced a method which they labeled calibration. What is this all about? The K-P framework utilizes various studies, expert opinion and data analysis to form a view on the numerical magnitude of a parameter. For instance, using the historical data of wages and income K-P have established that the parameter a in the Cobb-Douglas production function is around 0.64.
By incorporating the information on a with the information on real gross domestic product, the stock of capital and the number of workers employed one, can now extract the numerical values for the technology factor A. Once the technology factor A is extracted, it can be employed to assess the effect it has on fluctuations of various key economic data, so it is held. In their research K-P have demonstrated that a technology-induced shock can explain 70 percent of fluctuations in the postwar US data.
The introduction of calibration supposedly provides an answer to the Robert Lucas (1995 Nobel laureate) critique that questioned the economic analysis’s reliance on fixed parameters models to assess the implications of government policies on the economy. According to Lucas’s criticism, one cannot employ fixed parameters models in order to assess the effect of a change in government policy. A change in the government policy is going to alter the parameters in the real world. Hence, a fixed parameters model, which employs unchanged parameter estimates, is going to produce misleading results. (A change in government policy alters the behavior of participants in an economy. Fixed parameters models ignore that). On this, Lucas is in agreement with Ludwig von Mises.
According to Mises, “There are, in the field of economics, no constant relations, and consequently no measurement is possible.”
The calibration method, however, still assumes that there are parameters, which can be ascertained by means of historical data and expert opinions.
Technology Shocks Are Likely to Alter Human Conduct
What is the mechanism that converts the technology shock into boom-bust cycles in the K-P framework? A positive technology shock, according to K-P, means that with a given supply of capital and labor, the economy can generate more output.
Higher productivity leads to higher wages. This in turn raises workers’ willingness to work more and reduce their leisure. The higher return on capital gives rise to more capital investment. All this leads to economic boom and prosperity.
A recession is caused by a negative technology shock, which lowers the return on labor and capital. This in turn causes workers to work fewer hours and a decline in capital investment. Consequently, this leads to a fall in output; i.e., to an economic bust.
Notwithstanding the calibration method, K-P still employ a fixed parameters model to establish the importance of the technology shock in boom-bust economic cycles. Again, the calibration method is about establishing parameters not by means of conventional statistical methods, but by examining the historical data and expert opinion.
To suggest that a technological shock is not going to alter parameter a is to suggest that we are dealing with machines and not human beings. This implies that the K-P conclusion that a technology-induced shock can explain 70 percent of fluctuations in the postwar US data is questionable. Note, again, that K-P obtain this conclusion by means of a fixed parameters model; i.e., the parameter a in their model stays fixed.
(Even if one were to accept that the parameter a obtained by means of calibration is stable over time, this does not address the Lucas critique that change in government policies and other shocks such as the technology shock are going to affect human conduct and hence parameter a). In fact, the Lucas’s critique implies exactly what Mises had suggested, that in the field of economics, no constant relations exist, and consequently no measurement is possible.
Also, one could question the structure of the K-P model. K-P selected the Cobb-Douglas equation because Robert Solow, another Nobel laureate, used this in his research. It makes sense that if another model structure had been employed, the conclusions regarding the technology shock and the boom-bust cycle would have been different.
Economic Booms Are Not about Economic Prosperity
Contrary to K-P, an economic boom is not about economic prosperity and wealth generation, but about the mechanism that gives rise to activities that undermine the wealth generation process. The increase in these activities is labeled an economic boom. These types of activities emerge when resources are diverted from wealth-generating to non-wealth-generating activities, weakening the process of wealth generation.
If, for some reason, the diversion of resources is arrested, the various nonproductive activities that sprang up as a result of this diversion come under pressure; i.e., an economic bust emerges. According to Mises,
The boom squanders through malinvestment scarce factors of production…. its alleged blessings are paid for by impoverishment. The depression, on the other hand, is the way back to a state of affairs in which all factors of production are employed for the best possible satisfaction of the most urgent needs of the consumers.
Therefore, the key here is to identify the mechanism that diverts resources from wealth generators to non–wealth generators.
Central Bank Policies Are behind Boom-Bust Cycles
Central banks’ loose monetary policies are the mechanism that sets in motion the persistent diversion of resources from productive to nonproductive activities. Whenever the central bank loosens its stance, it does not generate economic prosperity, commonly labeled an economic boom, but rather the impoverishment of wealth producers. Whenever the central bank tightens its stance, the diversion of real resources toward various bubbles—i.e., nonproductive activities—is curtailed, bursting the bubbles, or causing what is called an economic bust.
The heart of what business cycles are all about is the process of the diversion of resources from productive to nonproductive activities by means of the loose monetary policies of the central bank. Furthermore, the policies of the central bank make boom-bust cycles inevitable.
Technological Changes Have Nothing to Do with Business Cycles
Technological change can be important to the process of wealth generation. A better technology can strengthen the process of wealth generation, and conversely, a negative technology shock can undermine the process of wealth generation.
None of this, however, has anything to do with business cycles. Instead, business cycles are about the process of diverting real resources from productive to nonproductive activities, a process set in motion by loose monetary policies. What K-P have introduced is not a novel way of understanding business cycles, but rather a different method of curve fitting. By means of calibration, various imaginary models can now supposedly be validated. If a particular functional form does not fit the data closely enough, then the function can be modified until the proper fit is established.
Conclusion
Technological changes are important in the development of wealth generation, but they have nothing to do with boom-bust cycles. The various mathematical models that supposedly established that technology is the key driving factor in business cycles do not address the real causes, such as the central bank policies, but rather describe the fluctuations of the data.
